activity
20182021
most citedGCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

787 citations · 1.2k across the 7 of their papers we have counts for

collaborators

12 papers

cs.DL2021

Science as a Public Good: Public Use and Funding of Science

Yian Yin, Yuxiao Dong, Kuansan Wang +2

Knowledge of how science is consumed in public domains is essential for a deeper understanding of the role of science in human society. While science is heavily supported by public…

cs.DL202135 cited

Is preprint the future of science? A thirty year journey of online preprint services

Boya Xie, Zhihong Shen, Kuansan Wang

Preprint is a version of a scientific paper that is publicly distributed preceding formal peer review. Since the launch of arXiv in 1991, preprints have been increasingly distribut…

cs.LG2020787 cited

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

Jiezhong Qiu, Qibin Chen, Yuxiao Dong +5

Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent develop…

cs.LG202071 cited

GPT-GNN: Generative Pre-Training of Graph Neural Networks

Ziniu Hu, Yuxiao Dong, Kuansan Wang +2

Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs usually requires abundant task-specific labeled data, w…

cs.DL2020

CORD-19: The COVID-19 Open Research Dataset

Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar +25

The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate t…

cs.LG202030 cited

Heterogeneous Graph Transformer

Ziniu Hu, Yuxiao Dong, Kuansan Wang +1

Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all…